| getThetaRatio {CalciOMatic} | R Documentation |
The function getThetaRatio estimates confidence intervals for
parameters a calcium dynamic model fitted to data estimated with the
ratiometric transformation. The way these CIs are computed depends on
the ciMode parameter.
getThetaRatio(calcium_ratio_fit,
ciMode = c("normalApprox", "likelihoodRatio"), ...)
calcium_ratio_fit |
an object of class "ratio_fit" |
ciMode |
should the normal approximation ("normal") or the
likelihood ratio ("ratio") be used to obtain the CI? |
... |
not used |
The ciMode argument specifies which approach to use to estimate
the CIs. If set to "normal", the quadratic approximation of the
log-likelihood applies, and the 95% CIs are given as
t(0.975,dof)*se(p), where t
is the Student quantile function, dof is the number of
degrees of freedom, se(p) is the standard error
associated to the estimation of parameter p (given by the
inverse of the square root of the diagonal of the hessian matrix
returned by "optim"). If ciMode is set to
"likelihoodRatio", we make use of the likelihood ratio
statistics (Davison, 2003).
A matrix with 2 rows and N columns, corresponding to the number
of parameters of the calcium dynamics model. Each column gives the
lower and upper bound of the 95% confidence interval for each
parameter.
Sebastien Joucla sebastien.joucla@parisdescartes.fr
Davison AC (2003), Statistical Models, Cambridge University Press
## Load the data from cockroach olfactory interneurons
data(inVitro)
## Calibrated parameters
R_min <- list(value=0.136, mean=0.136, se=0.00363, USE_se=TRUE)
R_max <- list(value=2.701, mean=2.701, se=0.151, USE_se=TRUE)
K_eff <- list(value=3.637, mean=3.637, se=0.729, USE_se=TRUE)
K_d <- list(value=0.583, mean=0.583, se=0.123, USE_se=TRUE)
## Create the data frame containing the physiological data
## (experiment #2, stimulation #2)
## G and s_ro are the respectively the gain of the CCD camera
## and the standard deviation of its read-out process
physioData <- ratioExpPhysio(dataset="inVitro",
expe=2, stim=2,
idxOn=10,
R_min=R_min, R_max=R_max,
K_eff=K_eff, K_d=K_d,
G=0.146, s_ro=16.4,
alphamethod=FALSE)
## Retrieve the calcium concentration from the data frame
Ca_noisy <- caFromDf(df = physioData,
numTransient = 2,
Plot = FALSE)
## Perform a ratiometric fit
physioRatioFit <- ratioFitFromCa(Ca = Ca_noisy,
t = attr(Ca_noisy,"Time"),
tOn = attr(Ca_noisy, "tOn"),
type = "mono",
AfterPeak = 14)
## Compute the confidence interval
## using the likelihood ratio statistics
CI <- getThetaRatio(physioRatioFit,
ciMode = "likelihoodRatio")
print(CI)